<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>2007-2422</journal-id>
<journal-title><![CDATA[Tecnología y ciencias del agua]]></journal-title>
<abbrev-journal-title><![CDATA[Tecnol. cienc. agua]]></abbrev-journal-title>
<issn>2007-2422</issn>
<publisher>
<publisher-name><![CDATA[Instituto Mexicano de Tecnología del Agua, Coordinación de Comunicación, Participación e Información]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2007-24222020000400179</article-id>
<article-id pub-id-type="doi">10.24850/j-tyca-2020-04-06</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Predicción de lluvias máximas para la república mexicana mediante modelos probabilísticos no estacionarios]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Álvarez-Olguín]]></surname>
<given-names><![CDATA[Gabriela]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Martínez-Ramírez]]></surname>
<given-names><![CDATA[Saúl]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Licona-Morán]]></surname>
<given-names><![CDATA[Brenda I. G.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Tecnológica de la Mixteca  ]]></institution>
<addr-line><![CDATA[Huajuapan de León Oaxaca]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Tecnológica de la Mixteca  ]]></institution>
<addr-line><![CDATA[Huajuapan de León Oaxaca]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad Tecnológica de la Mixteca  ]]></institution>
<addr-line><![CDATA[Huajuapan de León Oaxaca]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>08</month>
<year>2020</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>08</month>
<year>2020</year>
</pub-date>
<volume>11</volume>
<numero>4</numero>
<fpage>179</fpage>
<lpage>214</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S2007-24222020000400179&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S2007-24222020000400179&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S2007-24222020000400179&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen La predicción de eventos de lluvia máxima es la base del diseño de estructuras hidráulicas para la mitigación de inundaciones. Esta predicción se hace tradicionalmente a partir de métodos de análisis de frecuencias, que consisten en estudiar eventos pasados para estimar las probabilidades de ocurrencias futuras. Sin embargo, debido a que la variabilidad climática provoca cambios en la media y la varianza de las series de tiempo de lluvia, los eventos de diseño no son confiables si se estiman a partir de técnicas válidas para condiciones estacionarias. Para México, existe evidencia de que los patrones de lluvia se están modificando; en consecuencia, para que las predicciones sean confiables, se requieren aplicar métodos que contemplen cambios en las características estadísticas de los datos a través del tiempo. Por lo anterior, el objetivo de este trabajo fue estimar eventos de lluvia máxima en 24 horas, a través de modelos probabilísticos no estacionarios. Se analizaron 769 series a las que se aplicaron las pruebas de Pettitt, Mann-Kendall y Descomposición de Modos Empíricos por Conjuntos, para verificar su estacionaridad. Se propusieron diferentes modelos probabilísticos, en los que parámetros de las funciones de distribución lognormal, gamma, gumbel, weibull y general de valores extremos tienen como covariables al tiempo y al índice de oscilación decadal del Pacífico. Los resultados indican que para las series no estacionarias los modelos propuestos representan mejor la variabilidad de los datos que los modelos estacionarios convencionales.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract The prediction of maximum rainfall is the basis of the design of hydraulic structures for flood mitigation. This prediction is traditionally made from frequency analysis methods that consist of studying past events in order to define the probabilities of future occurrences. However, because climatic variability causes changes in the mean and variance of rainfall time series, design events are unreliable if they are estimated from techniques valid for stationary conditions. For Mexico, there is evidence that rainfall patterns are changing, consequently for predictions to be reliable, it is necessary to apply methods that contemplate changes in the statistical characteristics of the data over time. Therefore, the aim of this study was to estimate annual maximum 24-hour rainfall events associated with different return periods, through non-stationary probabilistic models. We analyzed 769 series to which were applied the Pettitt, Mann-Kendall and Ensemble Empirical Mode Decomposition tests to verify the seasonality. Different probabilistic models were proposed, in which parameters of the Lognormal, Gamma, Gumbel, Weibull and Generalized Extreme Value distribution have as covariates to time and Pacific Decadal Oscillation index. The results indicated that for the non-stationary series the proposed models represent the variability of the data better than conventional stationary models.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[análisis de frecuencias]]></kwd>
<kwd lng="es"><![CDATA[eventos hidrometeorológicos extremos]]></kwd>
<kwd lng="es"><![CDATA[oscilación decadal del Pacífico]]></kwd>
<kwd lng="es"><![CDATA[puntos de cambio]]></kwd>
<kwd lng="es"><![CDATA[tendencias y variabilidad climática]]></kwd>
<kwd lng="en"><![CDATA[Frequency analysis]]></kwd>
<kwd lng="en"><![CDATA[extreme hydro-meteorological events]]></kwd>
<kwd lng="en"><![CDATA[Pacific Decadal Oscillation]]></kwd>
<kwd lng="en"><![CDATA[change point and trend and climatic variability]]></kwd>
</kwd-group>
</article-meta>
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